Table 4

Performance of various cleavage prediction models to predict cleavage in pig prohormones
Performance Known Mammalian Human Logistic Human ANNd
Criteriaa Motif Logistic AAb AA Prop.c AA AA Prop.
True Positives 181 165 160 158 164 167
True Negatives 1520 1640 1724 1670 1735 1747
False Positives 329 209 125 179 114 102
False Negatives 54 70 75 77 71 68
Correct Classification 0.8162 0.8661 0.904 0.8772 0.9112 0.9184
Sensitivity 0.7702 0.7021 0.6809 0.6723 0.6979 0.7106
Specificity 0.8221 0.887 0.9324 0.9032 0.9383 0.9448
Positive predictive power 0.3549 0.4412 0.5614 0.4688 0.5899 0.6208
Negative predictive power 0.9657 0.9591 0.9583 0.9559 0.9607 0.9625
Correlation 0.4358 0.4856 0.5645 0.4944 0.5919 0.6184
AUC 0.8006 0.847 0.86 0.8186 0.8589 0.8802

a Performance criteria. True positives: number of correctly predicted cleaved sites; True negatives: number of correctly predicted non-cleaved sites; False positives: number of incorrectly predicted cleaved sites; False negatives: number of incorrectly predicted non-cleaved sites; Correct classification rate: number of correctly predicted sites divided by the total number of sites; Sensitivity (one minus false positive rate): number of true positives divided by the total number of sites cleaved; Specificity (one minus false negative rate): number of true negatives divided by the total number of sites not cleaved; Positive predictive power: number of true positives divided by the total number of sites predicted to be cleaved; Negative predictive power: number of true negatives divided by the total number of sites predicted to not be cleaved; Correlation coefficient: Mathew’s correlation coefficient between observed and predicted cleavage; and AUC: Area under the receiver operator characteristic or ROC curve relating sensitivity and 1-specificity.

b AA: models trained only on amino acids.

c AA prop: models trained with amino acids combined with the physicochemical properties of amino acids.

d ANN: artificial neural network approach.

Porter et al.

Porter et al. BMC Genomics 2012 13:582   doi:10.1186/1471-2164-13-582

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